259 citations · 972 across the 36 of their papers we have counts for
14 papers · 1 filter
Receptive Field Refinement for Convolutional Neural Networks Reliably Improves Predictive Performance
Mats L. Richter, Christopher Pal
Minimal changes to neural architectures (e.g. changing a single hyperparameter in a key layer), can lead to significant gains in predictive performance in Convolutional Neural Netw…
Attention-based Neural Cellular Automata
Mattie Tesfaldet, Derek Nowrouzezahrai, Christopher Pal
Recent extensions of Cellular Automata (CA) have incorporated key ideas from modern deep learning, dramatically extending their capabilities and catalyzing a new family of Neural C…
Robust Motion In-betweening
Félix G. Harvey, Mike Yurick, Derek Nowrouzezahrai +1
In this work we present a novel, robust transition generation technique that can serve as a new tool for 3D animators, based on adversarial recurrent neural networks. The system sy…
Action-Based Representation Learning for Autonomous Driving
Yi Xiao, Felipe Codevilla, Christopher Pal +1
Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creati…
Medical Imaging with Deep Learning: MIDL 2020 -- Short Paper Track
Tal Arbel, Ismail Ben Ayed, Marleen de Bruijne +3
This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (MIDL 2020), held in Montreal, Canada, 6-9…
Reinforced active learning for image segmentation
Arantxa Casanova, Pedro O. Pinheiro, Negar Rostamzadeh +1
Learning-based approaches for semantic segmentation have two inherent challenges. First, acquiring pixel-wise labels is expensive and time-consuming. Second, realistic segmentation…